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Assumptions of problem-solving methods

  • Richard Benjamins
  • Christine Pierret-Golbreich
Theoretical and General Issues
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1076)

Abstract

Assumptions of problem-solving methods refer to necessary applicability conditions of problem-solving methods, indicating that a problem-solving method is only applicable to realize a task, if the assumptions are met. In principle, such assumptions may refer to any kind of condition involved in a problem-solving method's applicability, including its required domain knowledge. In this paper, we propose a conceptual organization for assumptions of problem-solving methods and suggest a formal language to describe them. For illustration we take examples from the Propose & Revise problem-solving method and from diagnosis.

Keywords

Domain Knowledge Logical Combination Proof Obligation Knowledge Engineer User Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    A. Aamodt, B. Benus, C. Duursma, C. Tomlinson, R. Schrooten, and W. Van de Velde. Task features and their use in commonkads. Technical Report KADS-II/T1.5/VUB/TR/014/1.0, Free University of Brussels & University of Amsterdam & Lloyd's Register, 1992.Google Scholar
  2. 2.
    J. M. Akkermans, B. J. Wielinga, and A. Th. Schreiber. Steps in constructing problem-solving methods. In B. R. Gaines and M. A. Musen, editors, Proceedings of the 8th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. Volume 2: Shareable and Reusable Problem-Solving Methods, pages 29–1–29–21, Alberta, Canada, January 30–February 4 1994. SRDG Publications, University of Calgary.Google Scholar
  3. 3.
    V. R. Benjamins. Problem Solving Methods for Diagnosis. PhD thesis, University of Amsterdam, Amsterdam, The Netherlands, 1993.Google Scholar
  4. 4.
    V. R. Benjamins. Problem-solving methods for diagnosis and their role in knowledge acquisition. International Journal of Expert Systems: Research and Applications, 8(2):93–120, 1995.Google Scholar
  5. 5.
    B. Chandrasekaran. Design problem solving: A task analysis. AI Magazine, 11:59–71, 1990.Google Scholar
  6. 6.
    B. Chandrasekaran, T. R. Johnson, and J. W. Smith. Task-structure analysis for knowledge modeling. Communications of the ACM, 35(9):124–137, 1992.Google Scholar
  7. 7.
    L. Console and P. Torasso. Integrating models of the correct behaviour into abductive diagnosis. In L. C. Aiello, editor, Proc. ECAI-90, pages 160–166, London, 1990. ECCAI, Pitman.Google Scholar
  8. 8.
    D. Fensel. Assumptions and limitations of a problem-solving method: A case study. In B. R. Gaines and M. A. Musen, editors, Proceedings of the 8th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Alberta, Canada, 1995. SRDG Publications, University of Calgary.Google Scholar
  9. 9.
    D. Fensel, R. Straatman, and F. van Harmelen. The mincer metaphor: a new view on problem-solving methods for knowledge-based systems. Technical report, SWI, University of Amsterdam, Amsterdam, 1995.Google Scholar
  10. 10.
    J.H Gennari, S.W Tu, T.E Rotenfluh, and M.A. Musen. Mapping domains to methods in support of reuse. International Journal of Human-Computer Studies, 41:399–424, 1994.Google Scholar
  11. 11.
    C. Pierret-Golbreich. TASK model: a framework for the design of models of expertise and their operationalization. In B. R. Gaines and M. A. Musen, editors, Proceedings of the 8th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, pages 37.1–37.22. SRDG Publications, University of Calgary, 1994.Google Scholar
  12. 12.
    C. Pierret-Golbreich. Modular and reusable specifications in knowledge engineering: Formal specification of goals and their development. In Workshop on Knowledge Engineering Methods and Languages (KEML), 1996.Google Scholar
  13. 13.
    C. Pierret-Golbreich and I. De Louis. Task: Task centered representation for expert systems at the knowledge level. In Proc. of the 8th AISB-conference. Springer-Verlag, 1991.Google Scholar
  14. 14.
    C. Pierret-Golbreich and X. Talon. An algebraic specification of the dynamic behavior of knowledge-based systems. In B. R. Gaines and M. A. Musen, editors, Proceedings of the 9th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Alberta, Canada, 1995. SRDG Publications, University of Calgary.Google Scholar
  15. 15.
    A.R. Puerta, J. Egar, S. Tu, and M. Musen. A multiple-method shell for the automatic generation of knowledge acquisition tools. Knowledge Acquisition, 4:171–196, 1992.Google Scholar
  16. 16.
    William F. Punch and B. Chandrasekaran. An investigation of the roles of problem-solving methods in diagnosis. In Proc. of the Tenth International Workshop: Expert Systems and their Applications, pages 25–36, Avignon, France, 1990. EC2.Google Scholar
  17. 17.
    M. Reinders and B. Bredeweg. Strategic reasoning as a reflective task. In Proceedings of IMSA-92, pages 159–163, 1992.Google Scholar
  18. 18.
    M. Schmidt-Schauß. Computational Aspects of an Order-Sorted Logic with Term Declarations. Springer-Verlag, Berlin, Germany, 1989. Lecture Notes in Artificial Intelligence No. 395.Google Scholar
  19. 19.
    L. Steels. Components of expertise. AI Magazine, 11(2):28–49, Summer 1990.Google Scholar
  20. 20.
    A. ten Teije and F. van Harmelen. An extended spectrum of logical definitions for diagnostic systems. In Proceedings of DX-94 Fifth International Workshop on Principles of Diagnosis, 1994.Google Scholar
  21. 21.
    A. ten Teije and F. van Harmelen. An extended spectrum of logical definitions for diagnostic systems. Computational Intelligence, 1995. Submitted.Google Scholar
  22. 22.
    G. van Heijst. The Role of Ontologies in Knowledge Engineering. PhD thesis, University of Amsterdam, May 1995.Google Scholar
  23. 23.
    K. van Marcke. A generic tutoring environment. In L. C. Aiello, editor, Proc. of the Ninth European Conference on Artificial Intelligence, pages 655–660, London, UK, 1990. Pitman.Google Scholar
  24. 24.
    J. Vanwelkenhuysen and P. Rademakers. Mapping knowledge-level analysis onto a computational framework. In L. Aiello, editor, Proc. ECAI-90, pages 681–686, London, 1990. Pitman.Google Scholar
  25. 25.
    B. J. Wielinga, W. Van de Velde, A. Th. Schreiber, and J. M. Akkermans. The Common KADS framework for knowledge modelling. In B. R. Gaines, M. A. Musen, and J. H. Boose, editors, Proc. 7th Banff Knowledge Acquisition Workshop, volume 2, pages 31.1–31.29. SRDG Publications, University of Calgary, Alberta, Canada, 1992.Google Scholar
  26. 26.
    G. Yost. Configuring elevator systems. Technical report, Digital Equipment Corporation, 111 Locke Drive (LMO2/K11), Marlboro MA 02172, 1992.Google Scholar
  27. 27.
    Z. Zdrahal and E. Motta. An in-depth analysis of propose & revise problem solving methods. In B. R. Gaines and M. A. Musen, editors, Proceedings of the 9th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Alberta, Canada, 1995. SRDG Publications, University of Calgary.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Richard Benjamins
    • 1
  • Christine Pierret-Golbreich
    • 2
  1. 1.SWI, University of AmsterdamWB AmsterdamThe Netherlands
  2. 2.LRI, University of Paris-SudOrsay CedexFrance

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